1 Loading of data

1.1 Data set

First, we load, filter, and merge the data sets.

How does the data set looks like

1.2 Set tresholds

Applied tresholds are indicated by grey horizontal line.

1.2.1 Mean_Puncta_mito_AreaShape_Area

1.2.2 Mean_Puncta_mito_Number_Object_Number

1.2.3 mito_MeanArea

1.2.4 mito_MeanCount

1.2.5 mito_MeanLength

1.2.6 Branchpoints

1.3 Counts per sample

#Apply tresholds
data <- subset(data, Mean_Puncta_mito_AreaShape_Area < 200)
data <- subset(data, Mean_Puncta_mito_Number_Object_Number < 1200)
data <- subset(data, mito_MeanArea < 0.04)
data <- subset(data, mito_MeanCount < 0.2)
data <- subset(data, mito_MeanLength < 0.1)
data <- subset(data, Branchpoints < 200)

#Save data set
write.csv(data, file = "results/tables/data_mito.csv")

Cell counts per cell line:

#data <- read.csv("results/tables/data_mito.csv")
table(data$Metadata_SampleID)
## 
## i1JF-R1-018 iG3G-R1-039 i1E4-R1-003 iO3H-R1-005 i82A-R1-002 iJ2C-R1-015 
##         110         118         125         118         101         114 
## iM89-R1-005 iC99-R1-007 iR66-R1-007 iAY6-R1-003 iPX7-R1-001 i88H-R1-002 
##          87         105          89         167         131          76

Mean cell count:

mean(table(data$Metadata_SampleID))
## [1] 111.75

2 Visualize mitochondrial parameters

Various mitochondrial parameters are visualized for each patient-derived cell line as well as for the disease state Mean Ctrl levels are indicated by grey horizontal line.

2.1 mito Area

2.1.1 each sample

2.1.2 disease-state

2.2 mito Count

2.2.1 each sample

2.2.2 disease-state

2.3 mito Intensity

2.3.1 each sample

2.3.2 disease-state

2.4 mito Mean Area

2.4.1 each sample

2.4.2 disease-state

2.5 mito Mean Count

2.5.1 each sample

2.5.2 disease-state

2.6 mito Number Branch Ends

2.6.1 each sample

2.6.2 disease-state

2.7 mito Number Branchpoints

2.7.1 each sample

2.7.2 disease-state

2.8 mito Skeleton Length

2.8.1 each sample

2.8.2 disease-state

2.9 mito Mean Skeleton Length

2.9.1 each sample

2.9.2 disease-state

3 Statistical testing using linear mixed effects models

Nested approach (“Mitochondrial Parameter” ~ Disease_state + (1 | Disease_state:Metadata_SampleID)) to compensate for dependencies within the groups.

3.1 mito Area

## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: 
## Mean_Puncta_mito_AreaShape_Area ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  13185.5  13206.3  -6588.7  13177.5     1337 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2591 -0.7390 -0.1397  0.5596  3.6687 
## 
## Random effects:
##  Groups                          Name        Variance Std.Dev.
##  Disease_state:Metadata_SampleID (Intercept)   51.01   7.142  
##  Residual                                    1066.69  32.660  
## Number of obs: 1341, groups:  Disease_state:Metadata_SampleID, 12
## 
## Fixed effects:
##                  Estimate Std. Error t value
## (Intercept)        73.945      3.475  21.279
## Disease_statesPD    6.561      4.563   1.438
## 
## Correlation of Fixed Effects:
##             (Intr)
## Diss_sttsPD -0.761
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: Mean_Puncta_mito_AreaShape_Area
##                Chisq Df Pr(>Chisq)
## Disease_state 2.0668  1     0.1505

3.2 mito Count

## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: Mean_Puncta_mito_Number_Object_Number ~ Disease_state + (1 |  
##     Disease_state:Metadata_SampleID)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  19004.5  19025.3  -9498.2  18996.5     1337 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.8091 -0.8211 -0.1309  0.7812  2.5681 
## 
## Random effects:
##  Groups                          Name        Variance Std.Dev.
##  Disease_state:Metadata_SampleID (Intercept)  3299     57.44  
##  Residual                                    81869    286.13  
## Number of obs: 1341, groups:  Disease_state:Metadata_SampleID, 12
## 
## Fixed effects:
##                  Estimate Std. Error t value
## (Intercept)        565.65      28.35  19.955
## Disease_statesPD   -10.11      37.24  -0.272
## 
## Correlation of Fixed Effects:
##             (Intr)
## Diss_sttsPD -0.761
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: Mean_Puncta_mito_Number_Object_Number
##                Chisq Df Pr(>Chisq)
## Disease_state 0.0738  1     0.7859

3.3 mito Intensity

## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: Mean_Puncta_mito_Intensity_MeanIntensity_Corr_mito ~ Disease_state +  
##     (1 | Disease_state:Metadata_SampleID)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  -4093.7  -4072.9   2050.8  -4101.7     1337 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.1579 -0.7031 -0.1618  0.6287  4.3208 
## 
## Random effects:
##  Groups                          Name        Variance  Std.Dev.
##  Disease_state:Metadata_SampleID (Intercept) 0.0007024 0.02650 
##  Residual                                    0.0026666 0.05164 
## Number of obs: 1341, groups:  Disease_state:Metadata_SampleID, 12
## 
## Fixed effects:
##                  Estimate Std. Error t value
## (Intercept)       0.14448    0.01205  11.991
## Disease_statesPD -0.01822    0.01579  -1.154
## 
## Correlation of Fixed Effects:
##             (Intr)
## Diss_sttsPD -0.763
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: Mean_Puncta_mito_Intensity_MeanIntensity_Corr_mito
##                Chisq Df Pr(>Chisq)
## Disease_state 1.3317  1     0.2485

3.4 mito Mean Area

## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: mito_MeanArea ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  -9414.2  -9393.4   4711.1  -9422.2     1337 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6079 -0.6999 -0.2744  0.4379  3.8921 
## 
## Random effects:
##  Groups                          Name        Variance Std.Dev.
##  Disease_state:Metadata_SampleID (Intercept) 1.68e-06 0.001296
##  Residual                                    5.13e-05 0.007163
## Number of obs: 1341, groups:  Disease_state:Metadata_SampleID, 12
## 
## Fixed effects:
##                   Estimate Std. Error t value
## (Intercept)      0.0102080  0.0006527  15.639
## Disease_statesPD 0.0010092  0.0008580   1.176
## 
## Correlation of Fixed Effects:
##             (Intr)
## Diss_sttsPD -0.761
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: mito_MeanArea
##                Chisq Df Pr(>Chisq)
## Disease_state 1.3836  1     0.2395

3.5 mito Mean Count

## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: mito_MeanCount ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  -4425.0  -4404.2   2216.5  -4433.0     1337 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.5945 -0.8088 -0.2109  0.6250  2.9401 
## 
## Random effects:
##  Groups                          Name        Variance  Std.Dev.
##  Disease_state:Metadata_SampleID (Intercept) 7.809e-05 0.008837
##  Residual                                    2.116e-03 0.046002
## Number of obs: 1341, groups:  Disease_state:Metadata_SampleID, 12
## 
## Fixed effects:
##                   Estimate Std. Error t value
## (Intercept)       0.075443   0.004397  17.158
## Disease_statesPD -0.002436   0.005778  -0.422
## 
## Correlation of Fixed Effects:
##             (Intr)
## Diss_sttsPD -0.761
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: mito_MeanCount
##                Chisq Df Pr(>Chisq)
## Disease_state 0.1777  1     0.6733

3.6 mito Number Branch Ends

## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: ObjectSkeleton_NumberBranchEnds_mito_Skeleton ~ Disease_state +  
##     (1 | Disease_state:Metadata_SampleID)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  10055.6  10076.4  -5023.8  10047.6     1337 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6671 -0.7047 -0.1464  0.5690  4.9810 
## 
## Random effects:
##  Groups                          Name        Variance Std.Dev.
##  Disease_state:Metadata_SampleID (Intercept)   1.512   1.23   
##  Residual                                    104.186  10.21   
## Number of obs: 1341, groups:  Disease_state:Metadata_SampleID, 12
## 
## Fixed effects:
##                  Estimate Std. Error t value
## (Intercept)       16.4336     0.6966  23.592
## Disease_statesPD  -0.3257     0.9177  -0.355
## 
## Correlation of Fixed Effects:
##             (Intr)
## Diss_sttsPD -0.759
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: ObjectSkeleton_NumberBranchEnds_mito_Skeleton
##                Chisq Df Pr(>Chisq)
## Disease_state 0.1259  1     0.7227

3.7 mito Number Branchpoints

## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: Branchpoints ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  13178.2  13199.0  -6585.1  13170.2     1337 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6695 -0.7268 -0.1804  0.4718  4.2733 
## 
## Random effects:
##  Groups                          Name        Variance Std.Dev.
##  Disease_state:Metadata_SampleID (Intercept)   27.24   5.219  
##  Residual                                    1065.67  32.645  
## Number of obs: 1341, groups:  Disease_state:Metadata_SampleID, 12
## 
## Fixed effects:
##                  Estimate Std. Error t value
## (Intercept)        45.253      2.705  16.728
## Disease_statesPD    1.675      3.558   0.471
## 
## Correlation of Fixed Effects:
##             (Intr)
## Diss_sttsPD -0.760
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: Branchpoints
##                Chisq Df Pr(>Chisq)
## Disease_state 0.2217  1     0.6377

3.8 mito Skeleton Length

## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: 
## ObjectSkeleton_TotalObjectSkeletonLength_mito_Skeleton ~ Disease_state +  
##     (1 | Disease_state:Metadata_SampleID)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  18029.6  18050.4  -9010.8  18021.6     1337 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.5013 -0.7576 -0.1928  0.5112  4.5958 
## 
## Random effects:
##  Groups                          Name        Variance Std.Dev.
##  Disease_state:Metadata_SampleID (Intercept)   552     23.49  
##  Residual                                    39843    199.61  
## Number of obs: 1341, groups:  Disease_state:Metadata_SampleID, 12
## 
## Fixed effects:
##                  Estimate Std. Error t value
## (Intercept)       273.809     13.427  20.393
## Disease_statesPD   -6.965     17.691  -0.394
## 
## Correlation of Fixed Effects:
##             (Intr)
## Diss_sttsPD -0.759
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: ObjectSkeleton_TotalObjectSkeletonLength_mito_Skeleton
##               Chisq Df Pr(>Chisq)
## Disease_state 0.155  1     0.6938

3.9 mito Mean Skeleton Length

## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: 
## mito_MeanLength ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  -6350.7  -6329.9   3179.4  -6358.7     1337 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.7079 -0.7971 -0.1527  0.6045  2.9565 
## 
## Random effects:
##  Groups                          Name        Variance  Std.Dev.
##  Disease_state:Metadata_SampleID (Intercept) 1.187e-05 0.003445
##  Residual                                    5.049e-04 0.022471
## Number of obs: 1341, groups:  Disease_state:Metadata_SampleID, 12
## 
## Fixed effects:
##                   Estimate Std. Error t value
## (Intercept)       0.033490   0.001805  18.550
## Disease_statesPD -0.001349   0.002375  -0.568
## 
## Correlation of Fixed Effects:
##             (Intr)
## Diss_sttsPD -0.760
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: mito_MeanLength
##                Chisq Df Pr(>Chisq)
## Disease_state 0.3224  1     0.5702